Development of a mulberry core collection
originated in China to enhance germplasm
conservation
Zhang Yanfang
1, Hu Dechang
2*, Zuo Jincheng
2, Zhang Ping
2,
Wang Zhaohong
3and Chen Chuanjie
3Abstract: China, origin of mulberry, has rich genetic resources usually. High expense, limited space and wavering environment of usually conservation in vivo poses dangerous situation for mulberry. The concept of core collection could takes priority for conservation of mulberry. In this study, 560 accessions were used with 40 morphological descriptors and stratified sampling strategies for a core collection. The core collection consisted of 28 accessions, accounting for 5% of the whole collection. The core collection included seven accessions belonging to Morus alba, one accession belonging to M. alba var. macrophylla, four accessions belonging to M. atropurprea, one accession belonging to M. nigra, three accessions belonging to M. australis, seven accessions belonging to M. multicaulis, two accessions belonging to M. wittorum and three accessions belonging to M. bombycis. The quality of core collection exceeded the evalua-tion criteria and could be a prioritized collecevalua-tion for high efficient and long-term conservation for mulberry.
Keywords: Morus L., genetic resources, morphological marker, germplasm
conservation
Crop Breeding and Applied Biotechnology 19: 55-61, 2019 Brazilian Society of Plant Breeding. Printed in Brazil http://dx.doi.org/10.1590/1984- 70332019v19n1a08 ARTICLE *Corresponding author: E-mail: [email protected] ORCID: 0000-0002-1536-5091 Received: 12 February 2017 Accepted: 13 July 2018
1 Fruit Research Institute, Fujian Academy of
Agricultural Sciences, Fuzhou 350013, China
2 College of life science, Ludong University,
Shandong Yantai 264025, China
3 Sericulture Research Institute of Shandong
Province, Shandong Yantai 264002, China
INTRODUCTION
Natural silk fiber, known as ‘Queen of textile’, is treated as a type of luxurious asset produced by silkworm. Crude silk fiber exported from China accounted for over 80% of worldwide production. Mulberry, the sole food source for silkworm, plays an important role in sericulture industry. Increasing demand for natural silk fiber all over the world needs more silkworms, leading to the lack of mulberry leaves in China. Propagation of mulberry vegetatively, natural and artificial crossing have produced abundant genetic resources. Over 3000 genotypes in China were documented (Pan 2000). Mulberry is usually conserved in vivo, exposed to environmental degradation and disadvantageous climatic conditions, resulting in the loss of genetic resources easily. Moreover, conservation in vivo of all germplasms is unpractical and highly expensive in human and financial resources. Therefore, it has been an urgent and challenging task for mulberry germplasm conservation, causing a serious threat to sustainable sericulture industry.
Frankel (1984) and Brown (1989) proposed the concept of core collection, a limited set of accessions of whole collection with minimum repetitiveness and maximum genetic diversity of a species and its relatives. Owing to representative
number, core collection is a promising and efficient method for conservation of crops to reduce the expense and space. Core collections of many crops such as persimmon (Zhang et al. 2009), flax (Diederichsen et al. 2013), mungbean (Schafleitner et al. 2015), peach palm (Cristo-Araújo et al. 2015), apple (Liang et al. 2015), etc. have been developed. Core collection has been a preferential collection for high efficient and low cost conservation such as cassava (Escobar et al. 2000), European elms (Harvengt et al. 2004), and garlic (Keller et al. 2012).
An appropriate construction strategy is prerequisite to develop a core collection. van Hintum (2000) described a general procedure for developing a core collection in the following sections: i) identify the total sampling ratio; ii) divide all accessions in whole collection into distinct groups; iii) decide the sampling proportion within group and iv) select entries from each group. Many researchers proposed other methods for development of a core collection such as PowerCore (Kim et al. 2007), Mstrat (Gouesnard et al. 2001), stepwise clustering (Hu et al. 2000), least distance stepwise clustering (Wang et al. 2007). These different methods depend on some factors such as genetic diversity of species, the size of the whole collection, grouping of the whole collection and data type (i.e. phenotypic or molecular data). For example, in Brazil the cultivated area of soybean increased drastically, the level of genetic diversity in the soybean collection was low (Gwinner et al. 2017). Nevertheless, these methods could lay foundation on the study by van Hintum (2000).
Chen et al. (2008) selected 11 accessions as core collection of Morus multicaulis Perr from 46 accessions originated in Shandong and Hebei province, China. Zhang et al. (2011) defined a core collection of 16 entries from 73 Gelu ecotype mulberry accessions in China. Guruprasad et al. (2014) analyzed 850 mulberry accessions assembled from 23 countries with molecular and phenotypic markers, resulting in a core collection including 122 entries (about 14.4% of total sampling ration). These limited studies showed the small size of whole collection from China. In present study, we firstly used 560 accessions from Mulberry Genetic Resources Catalog (Sericultural Research Institute 1986) and Mulberry Varieties
Records in China (Sericultural Research Institute 1993) as whole collection in order to obtain an appropriate mulberry
core collection based on morphological descriptors in order to enhance germplasm conservation.
MATERIAL AND METHODS
Materials and data identification
Five hundred and sixty Chinese mulberry accessions were used as genetic materials. Forty descriptors were recorded on Mulberry Genetic Resources Catalog (Sericultural Research Institute 1993a) and Mulberry Varieties Records
in China (Sericultural Research Institute 1993b), which were
standardized and numbered according to Descriptors and
Data Standard for Mulberry issued by Ministry of Science
and Technology (Table 1) for development of core collection. All accessions have been grown in field at Shandong Institute of Sericulture, Yantai City, Shandong Province, China since the foundation of the institute in 1950’s.Plants were distributed in a randomized complete design. For each accession, three plants were grown. Plants were spaced 70~80 cm between the rows and 70~80 cm within the row. All these data were recorded in detail for this study and were obtained from corresponding author.
Development procedure of core collection
The development procedures included grouping principle, the total sampling ratio, sampling proportion within group and sampling method within group (Figure 1). Grouping principle was carried out in term of traditional classification based on morphological characteristics. All
Table 1. Forty descriptors used in establishment of mulberry core collection
Code Descriptors Code Descriptor
1 Branch shape 21 Leaf orientation
2 Branch length 22 Leaf color
3 Branch thickness 23 Leaf apex
4 Lateral branch number 24 Leaf margin
5 Branch color 25 Leaf base
6 Branching ability 26 Leaf brightness
7 Internode shape 27 Leaf smoothness
8 Internode length 28 Leaf crinkle
9 Lenticel shape 29 Leaf thickness
10 Lenticel size 30 Leaf length
11 Lenticel color 31 Leaf width
12 Lenticel number 32 Petiole length
13 Winter bud shape 33 Petiole thickness
14 Winter bud color 34 Floral sex
15 Shoot tip location form 35 Tassel length 16 Accessory bud number 36 Tassel number 17 Accessory bud size 37 Female style
18 Phyllotaxis 38 Fruit size
19 Leaf shape 39 Fruit number
accessions could be divided into eight groups i.e. eight ecotypes, including M. alba L., M. alba var. macrophylla Loud.,
M. atropurprea Roxb., M. nigra L., M. australis Poir., M. multicaulis Perr., M. wittorum Handelb-Mazett. and M. bombycis
Koidz. Six total sampling ratios, 5, 10, 15, 20, 25 and 30%, were compared. There were four sampling proportion within group including constant proportion (P strategy), logarithm proportion (S strategy), square root proportion (L strategy) and genetic diversity proportion (G strategy) (van Hintum 2000). The clustering distance was Euclidean distance, and the clustering method was Unweighted Pair Group Method with Arithmetic Mean (UPGMA) (Mohammadi and Prasanna 2003). Two sampling method within group were stepwise clustering (Hu et al. 2000) and least distance stepwise clustering (Wang et al. 2007). Thus, 48 candidate core collections were formed to define the most suitable core collection. At least one accession should be chosen from each group.
Evaluation of candidate core collections
In order to evaluate the efficiency of candidate core collections, eight parameters were selected including coefficient of variation (CV), ratio of phenotype retained (RPR), variance of phenotypic value (VPV), variance of phenotypic frequency (VPF), index of diversity (I), deviation of phenotypic mean (Dmean), deviation of phenotypic maximum (Dmax) and deviation of phenotypic minimum (Dmin) (Li et al. 2002). Significant differences of the above parameters were calculated by SPSS16.0 (P=0.05). Duncan’s multiple range test was used to compare the differences of these parameters among total sampling ratios, among sampling proportions within group and among sampling methods within group. Based on the result of multiple comparisons, each of parameters was allotted a rank. The results were expressed by average ranks of the different parameters (Reviewer: 3 Show how the “average rank” is calculated.). The same value of average rank showed no difference while lower value of average rank showed better efficiency by multiple comparison of total sampling ratio, grouping of all accessions, sampling proportion within group and sampling method within group (Li et al. 2002).
A homogeneity test (F-test) for variances and a t-test for means (P=0.05) were performed to determine the ultimate core collection. The mean difference percentage (MD%), variance difference percentage (VD%), variable rate (VR%), coincidence rate (CR%) and coverage (%) were used for validation of core collection. The criteria were listed as follows: (1) no more than 20% of the traits have different means significantly (at P=0.05) between the core collection and the whole collection; (2) the CR% retained by the core collection is no less than 80%; (3) MD% should lower while VD%, CR% and coverage (%) showed higher (Hu et al. 2000, Kim et al. 2007).
Table 2. Variance analysis of eight parameters for 48 candidate core collections
Parameters1 SV df SS MS F value P
Means coefficient of variation (CV)
Total sampling ratio 5 1673.773 334.755 654.958 0
Sampling proportion within group 3 17.553 5.851 11.448 0
Sampling method within group 1 13.893 13.893 27.183 0
Interaction 1 9942.298 9942.298 1.95E+04 0 Error 38 19.422 0.511 Total 48 11666.94 Means ratio of phenotype re-tained (RPR)
Total sampling ratio 5 0.099 0.02 248.145 0
Sampling proportion within group 3 0.001 0 2.122 0.114
Sampling method within group 1 0.004 0.004 51.277 0
Interaction 1 41.402 41.402 5.18E+05 0 Error 38 0.003 7.99E-05 Total 48 41.509 Means variance of phenotypic value (VPV)
Total sampling ratio 5 6.45E-06 1.29E-06 0.911 0.484
Sampling proportion within group 3 4.43E-06 1.48E-06 1.042 0.385
Sampling method within group 1 1.45E-06 1.45E-06 1.022 0.319
Interaction 1 47.985 47.985 3.39E+07 0
Error 38 5.39E-05 1.42E-06
Total 48 47.985
Means variance of phenotypic frequency (VPF)
Total sampling ratio 5 3.03E+07 6067688 160.327 0
Sampling proportion within group 3 1210761 403587.2 10.664 0
Sampling method within group 1 154643.8 154643.8 4.086 0.05
Interaction 1 4.68E+07 4.68E+07 1.24E+03 0
Error 38 1438141 37845.81
Total 48 8.00E+07
Means index of diversity (I)
Total sampling ratio 5 0.002 0 2.979 0.023
Sampling proportion within group 3 0.001 0 3.224 0.033
Sampling method within group 1 0.018 0.018 148.411 0
Interaction 1 43.944 43.944 3.61E+05 0 Error 38 0.005 0 Total 48 43.97 Means deviation of phenotypic mean (Dmean)
Total sampling ratio 5 0 6.77E-05 0.959 0.455
Sampling proportion within group 3 0 0 1.66 0.192
Sampling method within group 1 0 0 1.833 0.184
Interaction 1 0 0 2.869 0.099 Error 38 0.003 7.06E-05 Total 48 0.004 Means deviation of phenotypic maximum (Dmax)
Total sampling ratio 5 1.113 0.223 15.853 0
Sampling proportion within group 3 0.036 0.012 0.85 0.475
Sampling method within group 1 0.266 0.266 18.952 0
Interaction 1 11.028 11.028 785.583 0 Error 38 0.533 0.014 Total 48 12.976 Means deviation of phenotypic minimum (Dmin)
Total sampling ratio 5 0.92 0.184 48.18 0
Sampling proportion within group 3 0.006 0.002 0.519 0.672
Sampling method within group 1 0.076 0.076 19.977 0
Interaction 1 4.338 4.338 1.14E+03 0
Error 38 0.145 0.004
RESULTS AND DISCUSSION
Screening of efficient evaluation parameters for mulberry core collection originated in China
For total sampling ratio, CV, RPR, VPF, I, Dmax and Dmin had lower P value than 0.05, showing significant difference of core collections by different total sampling ratio; for sampling proportion ratio, CV and VPF had lower P value than 0.05, showing significant difference of core collections by different sampling proportion ratios. For sampling method within group, CV, RPR, I, Dmax and Dmin had lower P value than 0.05, showing significant difference of core collections by different sampling methods within group (Table 2). Therefore, we selected six other parameters for testing efficiency of sampling strategies including CV, RPR, VPF, I, Dmax and Dmin.
Development and evaluation of mulberry core collection originated in China
A core collection of Morus multicaulis Perr. had developed by Chen et al. (2008), containing 11 entries selected from 46 accessions originated in Shandong and Hebei province, China. Zhang et al. (2011) defined a core collection of 16 entries from 73 Gelu ecotype mulberry accessions in China. Guruprasad et al. (2014) developed a core collection of 122 entries by analyzed 850 mulberry accessions assembled from 23 countries. These core collections could not represent genetic diversity of mulberry in China. In our study, we used 560 characterized accessions originated in China as whole collection for higher representatives of core collection.
Given the total sampling ratio, 10% - 30% of whole collection was suggested (Brown 1989, van Hintum 2000). Various total sampling ratios were compared for suitable one because of genetic diversity of one crop, accession number of base collection, available management of genetic resources and data type (i.e. phenotypic or molecular data), etc (Balas et al. 2014, Leroy et al. 2014, Taniguchi et al. 2014). In previous studies, there was no information of effects of total sampling ratio on development of mulberry core collection (Chen et al. 2008, Zhang et al. 2011, Guruprasad et al. 2014). In our study, according to comparison of average ranks of six total sampling ratios, the lowest value of average rank was 13.73 when total sampling ratio was 5%, showing significant difference at P=0.05 (Table 3). Therefore, 5% was taken as suitable total sampling ratio.
Comparison of average ranks of four sampling proportions within group showed that the average rank was lowest and different significantly, when logarithm proportion was used as sampling proportion within group (Table 3). Logarithm Table 3. Comparison of average ranks of evaluation parameters
Parameters CV1 RPR1 VPF1 I1 D
max1 Dmin1 Average2
Total sampling ratio (%)
5 4.50 4.50 4.50 19.25 5.75 43.88 13.73 c 10 12.50 13.75 12.5 24.50 17.50 35.62 19.40 b 15 20.50 21.69 20.5 26.88 22.62 25.50 22.95 ab 20 28.75 29.06 28.75 27.88 28.12 17.25 26.64 ab 25 36.75 36.62 37.12 26.25 33.12 12.25 30.35 ab 30 44.00 41.38 43.62 22.25 39.88 12.50 33.94 a
Sampling proportion within group
Constant proportion 26.25 26.42 27.33 26.33 27.00 23.67 26.17 a
Logarithm proportion 22.25 22.50 22.17 19.58 24.42 23.58 22.42 c
Square root proportion 24.75 25.25 24.58 25.25 23.42 25.92 24.86 ab
Diversity proportion 24.75 23.83 23.92 26.83 23.17 24.83 24.56 b
Sampling method
within groups Stepwise clusteringLeast distance stepwise clustering 22.9626.04 28.4820.52 23.3325.67 12.5836.42 30.7518.25 29.2919.71 22.97 a26.03 a
1 See code in Table 2; 2 Different lowercase letters represented significant difference (P<0.05)
Table 4. Comparison of core collections and whole collection
VD (%)1 MD (%)2 CR
(%)3 (%)VR4 Coverage (%)
Core collection by stepwise clustering 30.0 a 10.0 a 90.3 a 110.1 a 85.1 a
Core collection by least distance stepwise clustering 32.5 a 2.5 b 91.8 a 114.1 a 85.3 a
proportion could be suitable for sampling within group. Comparison of two sampling methods within group showed that least distance stepwise clustering had lower value of average rank (26.03), but there was no significant different average rank between stepwise clustering and least distance stepwise clustering (Table 3). It showed that there was no difference of effects on efficiency of development of mulberry core collection between two methods of sampling within group. It was not in accordance with the results of Wang et al. (2007). Our results showed similarity between lowest hierarchical level and least distance because of distinct grouping and details of characterization of the whole collection. Previous studies also indicated that rational grouping could enhance the efficient sampling for core collections (van Hintum 1995, Zhang et al. 2000, Wang et al. 2011).
Stepwise clustering and least distance stepwise clustering were compared for selection of the ultimate core collection based on VD%, MD%, CR%, VR% and Coverage (%) (Table 4). The two core collections by clustering meet all validation criteria with MD % lower than 20% and CR% higher than 80%. Moreover, showed that least distance stepwise clustering showed higher MD% than stepwise clustering methods. Therefore, we confirmed that the ultimate core collection was established by least distance stepwise clustering.
The most suitable core collection could be one by least distance stepwise clustering when total sampling ratio was 5% and sampling proportion within group was logarithm proportion. All accessions of the ultimate core collection were listed in Table 5. There were 28 accessions from all
eight ecotypes including M. alba L., M. alba var. macrophylla Loud., M. atropurprea Roxb., M. nigra L., M. australis Poir.,
M. multicaulis Perr., M. wittorum Handelb-Mazett. and M. bombycis Koidz.
In conclusion, a systematic and suitable mulberry core collection originated in China is firstly developed. Compared with previous mulberry core collections, the larger size of whole collection, more scientific and systematic development strategies were defined. In addition, Mulberry core collection originated in China in our study took advantages of more ecotypes. The core collection can be considered as a preferential collection for conservation and characterization of mulberry.
ACKNOWLEDGEMENTS
This work was supported by National Natural Science Foundation of China under Grant No. 31000308; Yantai key research and development program under Grant No. 2016ZH064.
Table 5. The list of mulberry core collection
Number Accession name Group
1 Jianyeqingsang M. alba 2 Jiesang 3 li’ersang 4 Santaiyaosang 5 Xiangbaitiaosang 6 Yunsang No.2 7 Zhensang
8 Husang No.199 M.alba var. macrophylla
9 Beiqu No.7 M .atropurprea 10 E’jian No.13 11 E’sang No.2 12 Yongxinsang 13 Yaosang M. nigra 14 Chasang M. australis 15 Shuyasang 16 Yazhousang 17 Baiqingsang M. multicaulis 18 Haiyanmianqing 19 Koutouheilu 20 Miyanqing 21 Qingpisang 22 Shuaisang No.4 23 Zitengsang 24 Baiyuwang M. wittorum 25 Zhenonghuosang 26 Pingshan No.9 M. bombycis 27 Wo’ersang 28 Zhaiyesang REFERENCES
Balas FC, Osuna MD, Domínguez G, Pérez-Gragera F and López-Corrales M (2014) Ex situ conservation of underutilised fruit tree species: establishment of a core collection for Ficus carica L. using microsatellite markers (SSRs). Tree Genetics & Genomes 10: 703-710.
Brown AHD (1989) Core collections: a practical approach to genetic resources management. Genome 31: 818-24.
Chen JB, Huang Y, Zhang L, Zhao WG and Pan YL (2008) Construction of the core collection of Morus multicaulis Perr germplasm resources from Shandong and Hebei based on ISSR molecular markers. Science of Sericulture 34: 587-592.
Chen XF (2008) Investigation and research on current situation of wild mulberry germplasm in Mianning County. Journal of Anhui Agricultural Sciences 36: 14170-14171.
Cristo-Araújo M, Rodrigues DP, Astolfi-Filho S and Clement CR (2015) Peach palm core collection in Brazilian Amazonia. Crop Breeding and Applied Biotechnology 15: 18-25.
Diederichsen A, Kusters PM, Kessler D, Bainas Z and Gugel RK (2013) Assembling a core collection from the flax world collection maintained by plant gene resources of Canada. Genetic Resources and Crop Evolution 60: 1479-1485.
Escobar R, Manrique NC, Rios A, Muñoz L, Mafla G, Debouck D and Tohme J (2000) Implementation of the encapsulation-dehydration cryopreservation method for the cassava core collection. Infection & Immunity 68: 6233-6239.
Frankel OH (1984) Genetic perspectives of germplasm conservation. In Arber WK, Llimensee K, Peacock WJ and Starlinger P (eds) Genetic manipulation: impact on man and society. IPGRI Cambridge University Press, Rome, p. 161-170.
Gouesnard B, Bataillon TM, Decoux G. Rozale C, Schoen DJ and David JL (2001) Mstrat: an algorithm for building germplasm core collections by maximising allelic or phenotypic richness. Journal of Heredity 92: 93-94.
Guruprasad, Krishnan RR, Dandin SB and Naik VG (2014) Groupwise sampling: a strategy to sample core entries from RAPD marker data with application to mulberry. Trees 28: 723-731.
Gwinner R, Setotaw TA, Pasqual M, Santos JB, Zuffo AM, Zambiazzi EV and Bruzi AT (2017) Genetic diversity in Brazilian soybean germplasm. Crop Breeding and Applied Biotechnology 17: 373-381
Harvengt L, Meier-Dinkel A, Dumas E and Collin E (2004) Establishment of a cryopreserved gene bank of european elms. Canadian Journal of Forest Research 34: 43-55
Hu J, Zhu J and Xu HM (2000) Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theoretical and Applied Genetics 101: 264-268.
Keller ERJ, Zanke CD, Blattner FR, Kik C, StavělíkováH, Zámečník J, Esnault F, Kotlińska T, Solberg S and Miccolis V (2012) Establishment of a European core collection by cryopreservation and virus elimination in Garlic. Acta Horticulturae 969: 319-327.
Kim KW, Chung HK, Cho GT, Ma KH, Chandrabalan D, Gwag JG, Kim TS, Cho EG and Park YJ (2007) PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics 23: 2155-2162.
Leroy T, De Bellis F, Legnate H, Musoli P, Kalonji A, Loor Solórzano R G and Cubry P (2014) Developing core collections to optimize the management and the exploitation of diversity of the coffee Coffea
canephora. Genetica 142: 185-199.
Li ZC, Zhang HL, Zeng YW, Yang ZY, Shen SQ, Sun CQ and Wang XK (2002) Studies on sampling schemes for the establishment of core collection of rice landraces in Yunnan, China. Genetic Resources and Crop Evolution 49: 67-74.
Liang W, Dondini L, De Franceschi P, Paris R, Sansavini S and Tartarini S (2015) Genetic diversity, population structure and construction of a core collection of apple cultivars from Italian germplasm. Plant Molecular Biology Reporter 33: 458-473.
Mohammadi SA and Prasanna BM (2003) Analysis of genetic diversity in crop plants-salient statistical tools and considerations. Crop Science 43: 1235-1248.
Pan YL (2000) Progress and prospect of germplasm resources and breeding of mulberry. Acta Ecologica Sinica 26 (Suppl): 1-8.
Schafleitner R, Nair RM, Rathore A, Wang YW, Lin CY, Chu SH, Lin PY, Chang JC and Ebert AW (2015) The AVRDC-The World Vegetable Center mungbean (Vigna radiata) core and mini core collections. BMC Genomics 16: 344-354.
Sericultural Research Institute (1986) Mulberry genetic resources catalog. China Agriculture Press, Beijing, 90p.
Sericultural Research Institute (1993) Mulberry varieties records in China. China Agriculture Press, Beijing, 299p.
Taniguchi F, Kimura K, Saba T, Ogino A, Yamaguchi S and Tanaka J (2014) Worldwide core collections of tea (Camellia sinensis) based on SSR markers. Tree Genetics & Genomes 10: 1555-1565.
van Hintum ThJL (1995) Hierarchical approaches to the analysis of genetic diversity in crop plants. In Hodgkin T, Brown AHD, van Hintum ThJL and Morales EAV (eds) Core collections of plant genetic resources. John Wiley and Sons, Chichester, p. 23-34.
van Hintum TJL, Brown AHD, Spillane C and Hodgkin T (2000) Core collections of plant genetic resources. IPGRI, Rome, 48p. Wang JC, Hu J, Xu HM and Zhang S (2007) A strategy on constructing core
collections by least distance stepwise sampling. Theoretical and Applied Genetics 115: 1-8.
Wang YZ, Zhang JH, Sun HY, Ning N and Yang L (2011) Construction and evaluation of a primary core collection of apricot germplasm in China. Scientia Horticulturae 128: 311-319.
Zhang L, Chen JB, Huang Y, Shen XJ, Liu L, Zhao WG and Qiang S (2011) Screening of core germplasms of Gelu ecotype mulberry based on ISSR marker. Science of Sericulture 37: 380-388.
Zhang XR, Zhao YZ, Cheng Y, Feng XY, Guo QY, Zhou MD and Hodgkin T (2000) Establishment of sesame germplasm core collection in China. Genetic Resources and Crop Evolution 47: 273-279.
Zhang YF, Zhang QL, Yang Y and Luo ZR (2009) Development of Japanese persimmon core collection by genetic distance sampling based on SSR markers. Biotechnology & Biotechnological Equipment 23: 1474-1478.
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